Principal Machine Learning Engineer-Customer Data Platform
| Company | Splunk |
|---|---|
| Location | Washington, USA, California, USA |
| Salary | $162560 – $279400 |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Expert or higher |
Requirements
- Advanced degree (Master’s or Ph.D.) in Computer Science, Data Science, or a related field.
- 10+ years of experience in applied machine learning or related fields.
- Strong programming skills in languages such as Python, Java, C++, or Scala.
- Expertise in data engineering, iterative ML model development using automated ML pipelines, distributed computing, cloud platforms (e.g., AWS, GCP), and ML frameworks (e.g., SageMaker, EMR Serverless, Glue, TensorFlow, PyTorch).
- Strong problem-solving capabilities with analytical thinking.
- Leadership skills to manage teams and drive critical initiatives.
- Excellent communication skills for multi-functional collaboration.
- Project management expertise to supervise timelines and resource allocation effective.
Responsibilities
- Lead the development of end-to-end ML systems, including data preprocessing, model training, evaluation, and deployment in production environments.
- Find opportunities for improvement with our ML-Ops processes and practices and promote these successfully within the team.
- Architect robust ML pipelines and scalable infrastructure using cloud platforms and distributed computing technologies.
- Build frameworks for curating training data, model evaluation, and monitoring deployed systems.
- Champion cross-team collaborations and drive the adoption of AI/ML tools across organizations. Ensure ML pipelines align with DevOps and ML-Ops industry friendly standard practices.
- Design, implement, and optimize ML algorithms and models to deliver customer value and build training datasets using large volumes of real production data.
- Stay updated on advancements in ML techniques (e.g., LLMs, personalization algorithms) to explore innovative applications for business needs.
- Innovate by prototyping new ideas and scaling successful solutions into production systems.
- Work with product managers to translate business requirements into technical solutions.
- Act as a liaison between technical teams and non-technical counterparts to communicate complex insights effectively.
- Ensure models meet data governance and privacy regulations and compliance rules (GDPR, HIPAA, CCPA etc).
- Ensure fairness, transparency, and ethical considerations in ML models by addressing biases in algorithm.
Preferred Qualifications
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No preferred qualifications provided.